As the volume of seismic observation time-series data grows larger, web-based visualization schemes suffer from longer system response times. Although big data visualization schemes based on sampling and filtering can greatly reduce the data scale and shorten transmission time, what it gains in speed it loses in information. Progressive visualization has become an increasingly popular scheme because it can quickly “see” some results without having to wait for all the data, thus enabling users to grasp a data-change trend quickly and perceive the rules behind it. In this paper, a Cloudberry-based progressive real-time visualization schema for earthquake big data (PVSEBD) is proposed for the first time. It greatly shortens the transmission time of each data slice, improves the user interaction experience, and meets the long-term, large-scale visualization needs of earthquake consultation business. Because the correctness of average aggregation function (AVG) in progressive visualization is often not guaranteed, this paper proposes an innovative AVG translation rule solution based on the accumulability of the COUNT and SUM aggregation functions. The experimental results showed that PVSEBD automatically adjusts the amount of data returned each time according to size and has a shorter response time for each interaction compared with the solution based on the web-based visualization toolkit Portable Progressive Parallel Processing Pipeline (P5).